A bearing fault diagnosis method based on an improved depth residual network

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Journal of Vibration and Shock ›› 2021, Vol. 40 ›› Issue (20) : 247-254.

TIAN Kewei1, DONG Shaojiang1, JIANG Baojun1, PEI Xuewu1, TANG Baoping2, HU Xiaolin3, ZHAO Xingxin4

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Abstract

A new fault diagnosis method based on an improved depth residual network was proposed to solve the problems of rolling bearings in noisy environment with large interference and complex and changeable working conditions.Firstly, the vibration signal of rolling bearing was preprocessed to obtain data samples, which were divided into training set and test set.The attention-mechanism-based Squeeze and Excitation Network (SENet) structure was introduced into the residual neural network residual block to establish the connection between the feature extraction channels.The improved deep residual network model was located there.The labeled training set data were input into the improved diagnostic model for training.Finally, the trained diagnosis model was applied to the test set to output the identification results of each fault.In order to suppress overfitting, the original training samples were denoised.Meanwhile, the activation function LReLU and Dropout technique were introduced to improve the anti-interference ability of the model.In order to verify the diagnostic performance of the model, experimental data were selected for verification.The results show that the method has good fault diagnosis capability when the load changes and the signal is seriously polluted by noise.

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rolling bearing / bearing fault diagnosis / deep residual network / extrusion and excitation network

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TIAN Kewei, DONG Shaojiang, JIANG Baojun, PEI Xuewu, TANG Baoping, HU Xiaolin, ZHAO Xingxin. A bearing fault diagnosis method based on an improved depth residual network[J]. Journal of Vibration and Shock, 2021, 40(20): 247-254

References

［1］JIA F, LEI Y G, LIN J, et al.Deep neural networks: A promising tool for fault characteristic mining and intelligent diagnosis of rotating machinery with massive data［J］.Mechanical Systems and Signal Processing, 2016, 72/73:303-315.
［2］SHAO H，JIANG H，LIN Y，et al.A novel method for intelligent fault diagnosis of rolling bearings using ensemble deep auto-encoders［J］.Mechanical Systems and Signal Processing, 2018,102:278-297.
［3］QU J, ZHANG Z, GONG T.A novel intelligent method for mechanical fault diagnosis based on dual-tree complex wavelet packet transform and multiple classifier fusion［J］.Neurocomputing, 2016, 171:837-853.
［4］ALI J B, FNAIECH N, SAIDI L, et al.Application of empirical mode decomposition and artificial neural network for automatic bearing fault diagnosis based on vibration signals［J］.Applied Acoustics, 2015, 89(3):16-27.
［5］史庆军，郭晓振，刘德胜.基于特征量融合和支持向量机的轴承故障诊断［J］.电子测量与仪器学报,2019(33)10:104-111.
SHI Qingjun, GUO Xiaozhen, LIU Desheng.Bearing fault diagnosis based on eigenvalue fusion and support vector machine ［J］.Journal of Electronic Measurement and Instrumentation, 2019,33(10):104-111.
［6］HE M, HE D.Deep learning based approach for bearing fault diagnosis［J］.IEEE Trans.Ind.Appl.,2017,53:3057-3065.
［7］ABDELJABER O, AVIC O, KIRANYAZ S, et al.Real-time vibration-based structural damage detection using one dimensional convolutional neural networks［J］.Journal of Sound and Vibration, Volume,2017,388:154-170.
［8］LIU H，ZHOU J，ZHENG Y，et al.Fault diagnosis of rolling bearings with recurrent neural network-based autoencoders［J］.ISA Trans,2018, 77:167-178.
［9］HAO S J, GE F X, LI Y M, et al.Multisensor bearing fault diagnosis based on one-dimensional convolutional long short-term memory networks［J］.Measurement,2020,159(5786):107802.
［10］HE K M, ZHANG X Y REN S Q, et al.Deep residual learning for image recognition［C］∥Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR).Piscataway, NJ, USA: IEEE, 2016.
［11］XU B, WANG N, CHEN T, et al.Empirical evaluation of rectified activations in convolution network［J］.I CML Deep Learn,2015:1-5
［12］HU J, LI S, GANG S.Squeeze-and-excitation networks［C］.In: Proceedings of the IEEE conference on computer vision and pattern recognition,2018.
［13］郭亮,高宏力,张一文,等.基于深度学习理论的轴承状态识别研究［J］.振动与冲击,2016,35(12):166-170.
GUO Liang, GAO Hongli, ZHANG Yiwen, et al.Research on bearing state recognition based on deep learning theory［J］.Journal of Vibration and Shock,2016,35(12):166-170.
［14］LIN M, CHEN Q, YAN S.Network in network［J］.arXiv:1312.4400, 2013.
［15］DONG S J, WU W L, HE K, et al.Rolling bearing performance degradation assessment based on improved convolutional neural network with anti-interference［J］.Measurement, 2020, 15:1-12.
［16］LOU X, LOPARO K A.Bearing fault diagnosis based on wavelet transform and fuzzy inference［J］.Mechanical Systems and Signal Processing, 2004, 18(5):1077-1095.
［17］ZHANG W, PENG G L, LI C H, et al.A deep convolutional neural network with new training methods for bearing fault diagnosis under noisy environment and different working load［J］.Mechanical Systems and Signal Processing,2018, 100:439-453.
［18］ZHANG W, PENG G L, LI C H, et al.A new deep learning model for fault diagnosis with good anti-noise and domain adaptation ability on raw vibration signals［J］.Sensors, 2017, 17(2):425.
［19］杨平,苏燕辰,张振.基于卷积胶囊网络的滚动轴承故障诊断研究［J］.振动与冲击,2020,39(4):55-62.
YANG Ping, SU Yanchen, ZHANG Zhen.Research on rolling bearing fault diagnosis based on convolutional capsule network［J］.Journal of Vibration and Shock,2020,39(4):55-62.